Lines are interesting geometrical features commonly seen in indoor and urban environments. There is missing a complete benchmark where one can evaluate lines from a sequential stream of images in all its stages: Line detection, Line Association and Pose error. To do so, we present a complete and exhaustive benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by providing a plethora of complementary metrics. We have also labelled data from well-known SLAM datasets in order to have all in one poses and accurately annotated lines. In particular, we have evaluated 17 line detection algorithms, 5 line associations methods and the resultant pose error for aligning a pair of frames with several combinations of detector-association. We have packaged all methods and evaluations metrics and made them publicly available on web-page https://prime-slam.github.io/evolin/.

翻译：线段是室内与城市环境中常见的几何特征，但目前缺乏能够完整评估连续图像序列中各阶段（即线段检测、线段关联及位姿误差）的综合性基准。为此，我们针对SLAM前端中的视觉线段（涵盖RGB与RGBD两种模式）提出了一套完备详尽的基准测试，通过提供丰富的互补性评估指标，并结合已知SLAM数据集中的标注数据（确保统一坐标系下的位姿与精确线段标注），系统评估了17种线段检测算法、5种线段关联方法及其组合在帧间对齐中的位姿误差。所有方法与评估指标已整合封装，并公开发布于网站 https://prime-slam.github.io/evolin/。